Some establishments that offer goods for sale have incorporated a drive-thru arrangement that allows individuals to stay in their vehicles throughout the process of ordering and picking up the contents of orders. For example, some fast food restaurants offer a drive-thru window that allows customers to order food and a pick-up window that allows the customers to pick up the food after ordering. The efficient handling of the flow of vehicles through a drive-thru is vital in securing more revenue and retaining more customers.
To improve drive-thru efficiency, some establishments have incorporated remote or Internet-based order placement by customers. In particular, the customer who places a remote order can forego placing the order on-site and can instead pick up the order directly from a pick-up window. There are drawbacks, however, in current implementations of the remote ordering systems. In particular, the establishment starts preparing the order contents once the customer arrives at the pick-up window or otherwise notifies the establishment that he/she is ready to pick up the order. For example, a fast food restaurant can start to prepare food associated with a remotely placed food order once the customer arrives at the pick-up window of the restaurant. Accordingly, the customer must wait for the establishment to prepare and assemble the order.
Due to its increased customer throughput relative to traditional configurations, the side-by-side drive-thru configuration has become a standard configuration in many newly built fast food restaurants and other establishments, as well as a configuration to which many existing restaurants are migrating. While it has benefits regarding the maximum drive-thru customer per hour rate that a restaurant can achieve (thus reducing the number of “drive-off” occurrences in which a customer arrives, concludes that the line is too long or has to wait longer than planned, and so decides to leave), it presents new challenges to restaurant managers.
One such challenge is the determination of the right order sequence, as vehicles can become shuffled between the time the order is placed and the time the customer receives the order, due to the parallel nature of the configuration. Since the line starts as a single lane that splits into two separate lanes with ordering consoles, and then the two lanes merge again into a single lane for payment and pickup, the two separate ordering points and re-merging of the lanes can cause a mismatch between the sequence in which the orders were taken and the sequence of cars that arrive at the pay and pickup counters.
This “out of sequencing” can result in the wrong expenses charged to the customer and/or the delivery of the wrong food to the customer (contributing significantly to customer dissatisfaction). Even if the accuracy of the delivered orders is maintained, these out of sequence events result in significant time loss (inefficiency) as the employees re-sequence the orders to match the vehicles in the queue. With roughly 75% of the business of many fast food restaurants being drive-thru, improving operational efficiency and therefore increasing drive-thru volumes provides an opportunity for significant business impact.
In order to enhance efficiencies in drive-thru implementations, it is believed that analyzing data from a side-by-side (i.e., parallel dual order point) drive-thru may be useful in order to develop an automated method for accurately determining the post-merge vehicle sequence using a video camera. Such an approach can identify the cars at each order point and then tracks them through the merge into a single lane for payment and pickup. This approach can operate in a manner that a vehicle ID (Identification) is assigned to a vehicle at the order point through the restaurant point of sale (POS) system when an order is taken. The car ID associated with the vehicle is taken as an input to the computer vision algorithm and reported to indicate the merge event for this vehicle. When a car ID is sitting in the queue, the algorithm tries to detect the vehicle at the order point through a classifier.
This, however, is not always possible due to vehicle-to-vehicle occlusions as shown in FIG. 1, for example, which depicts occlusions from the signage near the order point as shown in the sample image 26, or due to delays in the car ID assignment as shown in the sample image 28, in which case the car will have left the order point by the time car ID is assigned. This results in missed detections of vehicles and can cause pending car IDs to stack up at the order point.